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Two-Level Multifactor Designs for Detecting the Presence of Interactions,
WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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A design optimality criterion, trL-optimality, is applied to the problem of designing two-level multifactor experiments to detect the presence of interactions among the controlled variables. We give rules for constructing TRL-optimal foldover designs and trL-optimal fractional factorial designs. Some results are given on the power of these designs for testing the hypothesis that there are no two-factor interactions. Augmentation of the trL-optimal designs produces designs that achieve a compromise between the criteria of D- optimality for parameter estimation in a first-order model and trL- optimality for detecting the lack of fit. We give an example to demonstrate an application to the sensitivity analysis of a computer model. Originator-supplied keywords include Compromise design D-optimality Experimental design First order design Foldover design Fractional factorial design Main effects design Minimum aberration Optimal design Orthogonal array Resolution IV design Screening design Sensitivity analysis TrL-optimality.
APPROVED FOR PUBLIC RELEASE